ManukaAnalysis

F1 Layout

Map indicates the layout of the F1 generation resulting from a cross between EC201 and EC103 parents. Column 1 is approximately lengthways facing north.

“Map of F1 Trees”

Height Diagram

Diagram indicates the areas of leaf collection regarding height. Each Tree had 10 leaves collected, 3 from Low, 4 from Mid and 3 from High. The first leaf sampled was measured twice for replication comparison.

“Diagram of leaf collection levels”

Import and Arrange Data

“Full.xlsx” contains measurement information from sampling with the Dualex (https://www.force-a.com/en/capteurs-optiques-optical-sensors/dualex-scientific-chlorophyll-meter/), including;

Surface content of chlorophyll in ?g/cm? (Chl)
Epidermal Flavoid content in absorbance units; Flavonol(Flav) and Anthocyanin(Anth)
Nitrogen Balance Index status (NBI)

It also contains information about the block position, the leaf height information, and presense or absence of flowering

Sheet “Dup” contains only the samples that were replicated.

# Import Data Measures
Data <-read.xlsx("Full.xlsx", sheetName ="Full")
head(Data)

##   Collection.Day Allocation Block Column Row group Group.ID Tree.ID Rep.
## 1              4         CG     0      0   0    20       OG      CG    N
## 2              4         CG     0      0   0    20       OG      CG    N
## 3              4         CG     0      0   0    20       OG      CG    N
## 4              4         CG     0      0   0    20       OG      CG    N
## 5              4         CG     0      0   0    20       OG      CG    N
## 6              4         CG     0      0   0    20       OG      CG    N
##   measure Height Flower    Chl  Flav  Anth   NBI
## 1       9      H      Y 28.220 2.418 0.205 11.67
## 2       3      L      Y 27.958 2.298 0.691 12.17
## 3      11      H      Y 33.727 2.458 0.527 13.72
## 4       4      L      Y 25.938 1.758 0.172 14.76
## 5      10      H      Y 36.205 2.283 0.270 15.86
## 6       6      M      Y 34.332 2.115 0.150 16.23

Data$Column = as.factor(Data$Column)
Data$Row = as.factor(Data$Row)

# Import Replicate Data
Dup <-read.xlsx("Full.xlsx", sheetName ="Dup")
head(Dup)

##   Collection.Day group Group.ID Tree.ID Rep. measure Height   Chl  Flav
## 1            2.0    23       60   IN4DV   Y1       1      L 1.916 2.363
## 2            1.5     3        1   IN4BT   Y1       1      L 3.124 2.300
## 3            2.0    17       54   IN4DL   Y2       2      L 3.414 1.826
## 4            1.5    32       28   IN4CP   Y2       2      L 4.097 1.943
## 5            2.0     9       46   IN4DC   Y1       1      L 4.909 1.928
## 6            1.5     5        3   IN4BW   Y1       1      L 4.924 1.848
##    Anth  NBI
## 1 0.174 0.81
## 2 0.191 1.36
## 3 0.112 1.87
## 4 0.051 2.11
## 5 0.103 2.55
## 6 0.165 2.66

#Isolate Crimson Glory Outgroup
CG = Data[c(1:11),]

#Isolate East Cape 201 Parent
EC201 = Data[c(12:21),]

#Isolate East Cape 103 Parent
EC103 = Data[c(22:33),]

#Isolate Offspring from the Parental Cross
F1 = Data[c(34:1825),]

Replicate Analysis

Replicates were taken by measuring a single leaf sample from each tree twice, in order to establish consistency and reliability of measurements with the Dualex.

Replicate Data Overview

##                Min.     1st Qu.      Median        Mean     3rd Qu.
## RepAnth  0.00100000  0.06500000  0.09850000  0.09799367  0.12525000
## RepChl   1.91600000 25.82475000 36.02700000 35.76269937 46.61000000
## RepFlav  1.05600000  1.76775000  1.99400000  1.96720570  2.19725000
## RepNBI   0.81000000 12.70250000 18.19000000 18.73398734 24.45750000
##                Max.
## RepAnth  0.25400000
## RepChl  59.67400000
## RepFlav  2.71400000
## RepNBI  43.49000000

Replicate Group means

## # A tibble: 2 x 5
##   Rep.    Anth   Chl  Flav   NBI
##   <fct>  <dbl> <dbl> <dbl> <dbl>
## 1 Y1    0.0983  34.9  1.98  18.2
## 2 Y2    0.0977  36.6  1.95  19.2

Anthocyanin Replicate Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##### Chlorphyll Replicate Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##### Flavonol Replicate Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##### Nitrogen Balance Replicate Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

There is no statistically significant differences between the two groups of measurements, this is a good sign indicative of the accuracy of the Dualex.

ANOVA of Replicates

## Analysis of Variance Table
## 
## Response: Dup$Chl
##            Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep.    1    218  218.33   1.033 0.3102
## Residuals 314  66366  211.36

## Analysis of Variance Table
## 
## Response: Dup$Flav
##            Df  Sum Sq  Mean Sq F value Pr(>F)
## Dup$Rep.    1  0.0521 0.052129  0.6506 0.4205
## Residuals 314 25.1596 0.080126

## Analysis of Variance Table
## 
## Response: Dup$Anth
##            Df  Sum Sq   Mean Sq F value Pr(>F)
## Dup$Rep.    1 0.00003 0.0000304  0.0151 0.9023
## Residuals 314 0.63303 0.0020160

## Analysis of Variance Table
## 
## Response: Dup$NBI
##            Df  Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep.    1    82.5  82.530   1.115 0.2918
## Residuals 314 23241.0  74.016

The absence of statistically significant results indicates that our replicates are likely to be consistent.

Allocation Analysis

Allocation refers to which group measurements were taken from, i.e. A Parental Tree (EC103 or EC201), Outgroup Tree (CG), Parental Offspring (F1)

##                Min.     1st Qu.      Median        Mean     3rd Qu.
## AllAnth  0.00100000  0.06800000  0.09600000  0.09772877  0.12400000
## AllChl   0.13000000 24.10200000 36.21000000 35.21443342 46.82300000
## AllFlav  1.05600000  1.80600000  1.97800000  1.96440493  2.12800000
## AllNBI   0.07000000 12.14000000 18.64000000 18.34807123 24.44000000
##                Max.
## AllAnth  0.69100000
## AllChl  59.90500000
## AllFlav  2.86100000
## AllNBI  49.17000000

Allocation Group Means

## # A tibble: 4 x 5
##   Allocation   Anth   Chl  Flav   NBI
##   <fct>       <dbl> <dbl> <dbl> <dbl>
## 1 CG         0.258   36.0  2.17  16.7
## 2 EC103      0.0588  52.6  1.60  33.1
## 3 EC201      0.0779  52.6  1.71  31.7
## 4 F1         0.0971  35.0  1.97  18.2

Boxplot comparing medians and measurement distributions

ANOVAs

## Analysis of Variance Table
## 
## Response: Data$Chl
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## Data$Allocation    3   6742 2247.21  9.9149 1.748e-06 ***
## Residuals       1821 412728  226.65                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: Data$Chl
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## Data$Allocation    3   6742 2247.21  9.9149 1.748e-06 ***
## Residuals       1821 412728  226.65                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: Data$Flav
##                   Df  Sum Sq Mean Sq F value    Pr(>F)    
## Data$Allocation    3   2.713 0.90423  14.755 1.708e-09 ***
## Residuals       1821 111.598 0.06128                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: Data$Anth
##                   Df Sum Sq  Mean Sq F value    Pr(>F)    
## Data$Allocation    3 0.3040 0.101343  47.474 < 2.2e-16 ***
## Residuals       1821 3.8873 0.002135                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

F1 Samples (Approx 3 years) appear more similar to that of the Crimson Glory plant than either/both parents - this is possibly due to age effects as CG is likely more similar in this regard being shorter (No age confirmed). ANOVAs incidate there is significant differences between the allocations - this is to be expected.

Parent Tree Analysis

## # A tibble: 2 x 5
##   Tree.ID   Anth   Chl  Flav   NBI
##   <fct>    <dbl> <dbl> <dbl> <dbl>
## 1 EC103   0.0588  52.6  1.60  33.1
## 2 EC201   0.0779  52.6  1.71  31.7

##              Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
## ParAnth  0.004000  0.048250  0.058000  0.067500  0.086750  0.156000
## ParChl  30.722000 50.356750 55.739000 52.598909 57.794750 59.757000
## ParFlav  1.293000  1.475000  1.594000  1.650273  1.821000  2.138000
## ParNBI  14.370000 29.872500 34.025000 32.481818 35.892500 44.880000

Anthocyanin Parent Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## 
##  Welch Two Sample t-test
## 
## data:  Parent$Anth by Parent$Tree.ID
## t = -1.2283, df = 19.107, p-value = 0.2342
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05154327  0.01340994
## sample estimates:
## mean in group EC103 mean in group EC201 
##          0.05883333          0.07790000

Chlorophyll Parent Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## 
##  Welch Two Sample t-test
## 
## data:  Parent$Chl by Parent$Tree.ID
## t = -0.003842, df = 17.804, p-value = 0.997
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -7.830954  7.802387
## sample estimates:
## mean in group EC103 mean in group EC201 
##            52.59242            52.60670

Flavonol Parent Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## 
##  Welch Two Sample t-test
## 
## data:  Parent$Flav by Parent$Tree.ID
## t = -1.141, df = 19.612, p-value = 0.2676
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.31409194  0.09215861
## sample estimates:
## mean in group EC103 mean in group EC201 
##            1.599833            1.710800

Nitrogen Parent Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## 
##  Welch Two Sample t-test
## 
## data:  Parent$NBI by Parent$Tree.ID
## t = 0.47573, df = 14.7, p-value = 0.6413
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.076667  7.987334
## sample estimates:
## mean in group EC103 mean in group EC201 
##            33.14333            31.68800

Interesting sample pattern here, Chl and NBI start low and work high, Flav does the opposite. Maybe accuracy of measurements?

Parental Cross Analysis (F1 Generation)

Investigating measures across the F1 cross population

## # A tibble: 6 x 5
##   Tree.ID   Chl   NBI   Anth  Flav
##   <fct>   <dbl> <dbl>  <dbl> <dbl>
## 1 IN4BT    34.5  16.2 0.110   2.17
## 2 IN4BV    27.5  13.8 0.121   2.01
## 3 IN4BW    24.8  12.5 0.127   1.99
## 4 IN4BX    37.6  18.4 0.0905  2.05
## 5 IN4BY    24.2  12.5 0.110   1.98
## 6 IN4BZ    32.0  15.6 0.106   2.09

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$Anth
##              Df Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Tree.ID  158 0.5253 0.0033247  1.8093 2.001e-08 ***
## Residuals  1633 3.0007 0.0018376                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#Boxplot Chlorophyll F1 FChl <- ggplot(F1) + aes(x = Tree.ID, y = Chl) + geom_boxplot() +geom_hline(yintercept = mean(F1$Chl), color = "purple4") + ylab("Chlorophyll Measure") + xlab("Tree ID") + ggtitle("Chlorophyll of F1 Population") + theme_classic();FChl #Mean Measures Chlorophyll FSChl <- ggplot(F12) + aes(x = Tree.ID, y = Chl) + geom_point() +geom_hline(yintercept = mean(F12$Chl)) + ylab(“Chlorophyll Measure”) + xlab(“Tree ID”) + ggtitle(“Chlorophyll of F1 population”) #Histogram of Chlorophyll FHChl <- ggplot(F1) + aes(x = Chl) + geom_histogram() #Display Chlorophyll Plots grid.arrange(FSChl, FHChl) #Chlorophyll ANOVA FCA = lm(F1$Chl ~ F1$Tree.ID) anova(FCA)

#Boxplot Flavanol F1 FFlav <- ggplot(F1) + aes(x = Tree.ID, y = Flav) + geom_boxplot() +geom_hline(yintercept = mean(F1$Flav), color = "purple4") + ylab("Flavanol Measure") + xlab("Tree ID") + ggtitle("Flavonol of F1 Population") + theme_classic();FFlav #Mean Measures Flavonol FSFlav <- ggplot(F12) + aes(x = Tree.ID, y = Flav) + geom_point() +geom_hline(yintercept = mean(F12$Flav)) + ylab(“Flavonol Measure”) + xlab(“Tree ID”) + ggtitle(“Flavonol of F1 population”) #Histogram of Flavonol FHFlav <- ggplot(F1) + aes(x = Flav) + geom_histogram() #Display Chlorophyll Plots grid.arrange(FSFlav, FHFlav) #Flavonol ANOVA FFA = lm(F1$Flav ~ F1$Tree.ID) anova(FFA)

#Boxplot NBI F1 FNBI <- ggplot(F1) + aes(x = Tree.ID, y = NBI) + geom_boxplot() +geom_hline(yintercept = mean(F1$NBI), color = "purple4") + ylab("NBI Measure") + xlab("Tree ID") + ggtitle("NBI of F1 Population") + theme_classic();FNBI #Mean Measures NBI FSNBI <- ggplot(F12) + aes(x = Tree.ID, y = NBI) + geom_point() +geom_hline(yintercept = mean(F12$NBI)) + ylab(“NBI Measure”) + xlab(“Tree ID”) + ggtitle(“NBI of F1 population”) #Histogram of NBI FHNBI <- ggplot(F1) + aes(x = NBI) + geom_histogram() #Display NBI Plots grid.arrange(FSNBI, FHNBI) #NBI ANOVA FNA = lm(F1$NBI ~ F1$Tree.ID) anova(FNA)


<img src="Analysis_files/figure-html/investigating measure numbers of random samples-1.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-2.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-3.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-4.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-5.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-6.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-7.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-8.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-9.png" width="672" /><img src="Analysis_files/figure-html/investigating measure numbers of random samples-10.png" width="672" />

#### Height Analysis

# A tibble: 3 x 5

Height Anth Chl Flav NBI

1 H 0.105 33.9 1.93 17.9

2 L 0.0968 35.6 1.98 18.5

3 M 0.0913 35.3 1.98 18.1


<img src="Analysis_files/figure-html/Height Analysis-1.png" width="672" />

Analysis of Variance Table

Response: F1$Anth ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 0.0526 0.0262759 13.533 1.468e-06 * ## Residuals 1789 3.4735 0.0019416
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Call:

lm(formula = F1$Anth ~ F1$Height)

Residuals:

Min 1Q Median 3Q Max

-0.10400 -0.02878 -0.00056 0.02666 0.37800

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 0.105002 0.001973 53.232 < 2e-16 *## F1$HeightL -0.008225 0.002622 -3.137 0.00173 ## F1$HeightM -0.013666 0.002630 -5.197 2.26e-07***

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Residual standard error: 0.04406 on 1789 degrees of freedom

Multiple R-squared: 0.0149, Adjusted R-squared: 0.0138

F-statistic: 13.53 on 2 and 1789 DF, p-value: 1.468e-06

Analysis of Variance Table

Response: F1$Chl ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 853 426.30 1.8605 0.1559

Residuals 1789 409911 229.13

Analysis of Variance Table

Response: F1$Flav ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 0.915 0.45758 7.4943 0.0005739 * ## Residuals 1789 109.232 0.06106
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$NBI ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 100 50.044 0.6992 0.4971

Residuals 1789 128039 71.570


#### Row Analysis

# A tibble: 6 x 5

Row Chl NBI Anth Flav

1 1 35.6 17.3 0.107 2.09

2 10 30.5 15.7 0.112 1.98

3 11 29.9 15.0 0.112 2.04

4 12 34.6 18.7 0.0968 1.84

5 13 31.3 17.5 0.107 1.86

6 14 35.0 17.9 0.0888 1.98

Row Chl NBI Anth

17 :1 Min. :32.13 Min. :17.14 Min. :0.1163

0 :0 1st Qu.:32.13 1st Qu.:17.14 1st Qu.:0.1163

1 :0 Median :32.13 Median :17.14 Median :0.1163

10 :0 Mean :32.13 Mean :17.14 Mean :0.1163

11 :0 3rd Qu.:32.13 3rd Qu.:17.14 3rd Qu.:0.1163

12 :0 Max. :32.13 Max. :17.14 Max. :0.1163

(Other):0

Flav

Min. :1.887

1st Qu.:1.887

Median :1.887

Mean :1.887

3rd Qu.:1.887

Max. :1.887

Row Chl NBI Anth

1 : 1 Min. :29.19 Min. :14.38 Min. :0.07868

10 : 1 1st Qu.:32.02 1st Qu.:16.44 1st Qu.:0.08535

11 : 1 Median :34.38 Median :17.81 Median :0.09354

14 : 1 Mean :34.97 Mean :18.12 Mean :0.09677

15 : 1 3rd Qu.:37.05 3rd Qu.:19.46 3rd Qu.:0.10917

16 : 1 Max. :43.89 Max. :23.16 Max. :0.12239

(Other):33

Flav

Min. :1.820

1st Qu.:1.931

Median :1.974

Mean :1.973

3rd Qu.:2.037

Max. :2.092

Row Chl NBI Anth

12 :1 Min. :31.33 Min. :16.99 Min. :0.08183

13 :1 1st Qu.:34.20 1st Qu.:17.74 1st Qu.:0.09326

23 :1 Median :35.08 Median :18.29 Median :0.09747

32 :1 Mean :35.09 Mean :18.48 Mean :0.09702

41 :1 3rd Qu.:36.36 3rd Qu.:19.10 3rd Qu.:0.10265

45 :1 Max. :38.42 Max. :20.71 Max. :0.10721

(Other):4

Flav

Min. :1.839

1st Qu.:1.898

Median :1.944

Mean :1.941

3rd Qu.:1.995

Max. :2.032

[1] 0.0001562666

[1] 6.039853e-05

[1] 14.76679

[1] 4.10476

[1] 0.004848629

[1] 0.004125519

[1] 5.602987

[1] 1.155704

Welch Two Sample t-test

data: R4$Anth and R3$Anth

t = 0.081174, df = 22.551, p-value = 0.936

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

-0.006306855 0.006821440

sample estimates:

mean of x mean of y

0.09702315 0.09676586

Welch Two Sample t-test

data: R4$Chl and R3$Chl

t = 0.13309, df = 27.683, p-value = 0.8951

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

-1.702350 1.938809

sample estimates:

mean of x mean of y

35.08876 34.97053

Welch Two Sample t-test

data: R4$Flav and R3$Flav

t = -1.3877, df = 14.921, p-value = 0.1856

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

-0.08156262 0.01725693

sample estimates:

mean of x mean of y

1.941185 1.973338

Welch Two Sample t-test

data: R4$Flav and R3$Flav

t = -1.3877, df = 14.921, p-value = 0.1856

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

-0.08156262 0.01725693

sample estimates:

mean of x mean of y

1.941185 1.973338


There appears to be no signficant differences between Rows with 3 trees and rows with 4 trees for any of the measures. 

#### Row Plots and ANOVAs

<img src="Analysis_files/figure-html/unnamed-chunk-4-1.png" width="672" />

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


<img src="Analysis_files/figure-html/unnamed-chunk-4-2.png" width="672" />

Analysis of Variance Table

Response: F1$Anth ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Row 49 0.2346 0.0047878 2.5339 4.261e-08 * ## Residuals 1742 3.2914 0.0018895
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1


<img src="Analysis_files/figure-html/unnamed-chunk-4-3.png" width="672" />

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


<img src="Analysis_files/figure-html/unnamed-chunk-4-4.png" width="672" />

Analysis of Variance Table

Response: F1$Chl ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Row 49 21394 436.61 1.9533 0.0001046 * ## Residuals 1742 389370 223.52
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1


<img src="Analysis_files/figure-html/unnamed-chunk-4-5.png" width="672" />

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


<img src="Analysis_files/figure-html/unnamed-chunk-4-6.png" width="672" />

Analysis of Variance Table

Response: F1$Flav ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Row 49 8.215 0.167646 2.865 3.11e-10 * ## Residuals 1742 101.933 0.058515
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1


<img src="Analysis_files/figure-html/unnamed-chunk-4-7.png" width="672" />

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


<img src="Analysis_files/figure-html/unnamed-chunk-4-8.png" width="672" />

Analysis of Variance Table

Response: F1$NBI ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Row 49 7866 160.527 2.325 8.14e-07 * ## Residuals 1742 120273 69.043
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1


#### Column Analysis

# A tibble: 4 x 5

Column Chl NBI Anth Flav

1 1 33.8 17.2 0.0945 2.01

2 2 35.4 18.6 0.0992 1.94

3 3 36.0 18.7 0.0990 1.96

4 4 34.3 18.7 0.0912 1.88

Analysis of Variance Table

Response: F1$Anth ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Column 3 0.0122 0.0040655 2.0687 0.1024

Residuals 1788 3.5138 0.0019652

Analysis of Variance Table

Response: F1$Chl ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Column 3 1586 528.66 2.3101 0.07453 .

Residuals 1788 409178 228.85

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$Flav ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Column 3 2.305 0.76823 12.737 3.087e-08 * ## Residuals 1788 107.843 0.06031
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$NBI ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Column 3 821 273.626 3.8427 0.009333 ## Residuals 1788 127318 71.207
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1


<img src="Analysis_files/figure-html/Column Analysis-1.png" width="672" />

Analysis of Variance Table

Response: F1$Anth ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 0.05255 0.0262759 14.9537 3.788e-07 ## F1$Column 3 0.01228 0.0040942 2.3300 0.0727244 . ## F1$Row 49 0.23127 0.0047199 2.6861 6.175e-09

F1$Height:F1$Column 6 0.02055 0.0034247 1.9490 0.0699958 .

F1$Height:F1$Row 98 0.20024 0.0020433 1.1628 0.1394606

F1$Column:F1$Row 106 0.28473 0.0026861 1.5287 0.0006952 * ## F1$Height:F1$Column:F1$Row 212 0.41375 0.0019517 1.1107 0.1490621
## Residuals 1315 2.31066 0.0017572
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$Anth ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 0.05255 0.0262759 14.2135 7.586e-07 * ## F1$Row 49 0.23404 0.0047763 2.5837 2.178e-08 * ## F1$Height:F1$Row 98 0.20394 0.0020810 1.1257 0.1942
## Residuals 1642 3.03551 0.0018487
## — ## Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$Chl ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 853 426.30 2.0422 0.130152

F1$Column 3 1577 525.57 2.5178 0.056677 . ## F1$Row 49 21813 445.16 2.1326 1.244e-05 ## F1$Height:F1$Column 6 1282 213.61 1.0233 0.408244
## F1$Height:F1$Row 98 27033 275.85 1.3215 0.022708
## F1$Column:F1$Row 106 24037 226.77 1.0863 0.265386
## F1$Height:F1$Column:F1$Row 212 59670 281.46 1.3484 0.001427

Residuals 1315 274499 208.74

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$Chl ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 853 426.30 1.9372 0.14444

F1$Row 49 21279 434.27 1.9734 8.34e-05 *** ## F1$Height:F1$Row 98 27286 278.43 1.2652 0.04464 *
## Residuals 1642 361346 220.06

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$Chl ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Row 49 21394 436.61 1.9533 0.0001046 * ## Residuals 1742 389370 223.52
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$Flav ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 0.915 0.45758 9.3660 9.143e-05 ## F1$Column 3 2.323 0.77447 15.8523 3.957e-10 ## F1$Row 49 8.139 0.16611 3.4001 1.213e-13***

F1$Height:F1$Column 6 1.004 0.16728 3.4240 0.002334 **

F1$Height:F1$Row 98 5.743 0.05860 1.1995 0.096365 .

F1$Column:F1$Row 106 14.790 0.13953 2.8559 < 2.2e-16 ** ## F1$Height:F1$Column:F1$Row 212 12.988 0.06126 1.2540 0.012358
## Residuals 1315 64.245 0.04886
## — ## Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$NBI ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Height 2 100 50.044 0.7850 0.4563286

F1$Column 3 818 272.802 4.2793 0.0051365 ** ## F1$Row 49 7816 159.511 2.5021 8.551e-08 ## F1$Height:F1$Column 6 792 132.004 2.0707 0.0539220 .
## F1$Height:F1$Row 98 8175 83.418 1.3085 0.0268723
## F1$Column:F1$Row 106 7765 73.250 1.1490 0.1510179
## F1$Height:F1$Column:F1$Row 212 18842 88.877 1.3942 0.0004396 * ## Residuals 1315 83831 63.750
## — ## Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1$NBI ## Df Sum Sq Mean Sq F value Pr(>F) ## F1$Row 49 7866 160.527 2.3456 6.383e-07 * ## F1$Column 3 789 263.021 3.8432 0.00934 ## F1$Row:F1$Column 106 7724 72.864 1.0647 0.31303
## Residuals 1633 111761 68.439
## — ## Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1


Controlling for Height,Column,Row effects

Analysis of Variance Table

Response: Dat$Anth4 ## Df Sum Sq Mean Sq F value Pr(>F) ## Dat$Height 2 0.00000 0.0000012 0.0007 0.9993236

Dat$Column 3 0.00900 0.0029994 1.7070 0.1637135 ## Dat$Row 49 0.00061 0.0000125 0.0071 1.0000000

Dat$Height:Dat$Column 6 0.02055 0.0034247 1.9490 0.0699958 .

Dat$Height:Dat$Row 98 0.20024 0.0020433 1.1628 0.1394606

Dat$Column:Dat$Row 106 0.28473 0.0026861 1.5287 0.0006952 * ## Dat$Height:Dat$Column:Dat$Row 212 0.41375 0.0019517 1.1107 0.1490621
## Residuals 1315 2.31066 0.0017572
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: Dat$Chl3 ## Df Sum Sq Mean Sq F value Pr(>F) ## Dat$Height 2 737 368.65 1.7660 0.171413

Dat$Column 3 1956 652.10 3.1239 0.025073 * ## Dat$Row 49 155 3.17 0.0152 1.000000

Dat$Height:Dat$Column 6 1282 213.61 1.0233 0.408244

Dat$Height:Dat$Row 98 27033 275.85 1.3215 0.022708 *

Dat$Column:Dat$Row 106 24037 226.77 1.0863 0.265386

Dat$Height:Dat$Column:Dat$Row 212 59670 281.46 1.3484 0.001427 ## Residuals 1315 274499 208.74
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: Dat$Flav5 ## Df Sum Sq Mean Sq F value Pr(>F) ## Dat$Height 2 0.001 0.000491 0.0101 0.989993

Dat$Column 3 0.012 0.004029 0.0825 0.969582 ## Dat$Row 49 0.215 0.004387 0.0898 1.000000

Dat$Height:Dat$Column 6 1.004 0.167281 3.4240 0.002334 **

Dat$Height:Dat$Row 98 5.743 0.058602 1.1995 0.096365 .

Dat$Column:Dat$Row 106 14.790 0.139527 2.8559 < 2.2e-16 ** ## Dat$Height:Dat$Column:Dat$Row 212 12.988 0.061264 1.2540 0.012358
## Residuals 1315 64.245 0.048855
## — ## Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: Dat$NBI4 ## Df Sum Sq Mean Sq F value Pr(>F) ## Dat$Height 2 80 39.936 0.6264 0.5346458

Dat$Column 3 50 16.555 0.2597 0.8544529 ## Dat$Row 49 22 0.451 0.0071 1.0000000

Dat$Height:Dat$Column 6 792 132.004 2.0707 0.0539220 .

Dat$Height:Dat$Row 98 8175 83.418 1.3085 0.0268723 *
## Dat$Column:Dat$Row 106 7765 73.250 1.1490 0.1510179

Dat$Height:Dat$Column:Dat$Row 212 18842 88.877 1.3942 0.0004396 * ## Residuals 1315 83831 63.750
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1












#### Looking at controlled data

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


<img src="Analysis_files/figure-html/unnamed-chunk-6-1.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-2.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-3.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-4.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-5.png" width="672" />

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


<img src="Analysis_files/figure-html/unnamed-chunk-6-6.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-7.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-8.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-9.png" width="672" />

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


<img src="Analysis_files/figure-html/unnamed-chunk-6-10.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-11.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-12.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-13.png" width="672" />

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


<img src="Analysis_files/figure-html/unnamed-chunk-6-14.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-15.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-16.png" width="672" /><img src="Analysis_files/figure-html/unnamed-chunk-6-17.png" width="672" />

#### Correlations Between Measures

Comparing correlations between the 4 dualex measures

<img src="Analysis_files/figure-html/compare-1.png" width="672" />

Analysis of Variance Table

Response: Anth4

Df Sum Sq Mean Sq F value Pr(>F)

Chl3 1 0.30271 0.302706 192.4807 < 2.2e-16 ## Flav5 1 0.05792 0.057917 36.8277 1.572e-09

NBI4 1 0.01331 0.013306 8.4608 0.0036737 **

Chl3:Flav5 1 0.02196 0.021955 13.9607 0.0001925 ## Chl3:NBI4 1 0.03008 0.030085 19.1299 1.292e-05

Flav5:NBI4 1 0.00791 0.007905 5.0266 0.0250834 *
## Chl3:Flav5:NBI4 1 0.00005 0.000055 0.0348 0.8519736

Residuals 1784 2.80562 0.001573

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: Chl3

Df Sum Sq Mean Sq F value Pr(>F)

Anth4 1 36383 36383 1.9357e+04 < 2.2e-16 ## Flav5 1 383 383 2.0354e+02 < 2.2e-16

NBI4 1 342014 342014 1.8196e+05 < 2.2e-16 ## Anth4:Flav5 1 569 569 3.0285e+02 < 2.2e-16

Anth4:NBI4 1 115 115 6.1416e+01 7.879e-15 ## Flav5:NBI4 1 6552 6552 3.4859e+03 < 2.2e-16

Anth4:Flav5:NBI4 1 1 1 3.2830e-01 0.5667

Residuals 1784 3353 2

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: Flav5

Df Sum Sq Mean Sq F value Pr(>F)

Anth4 1 2.455 2.455 198.4244 < 2.2e-16 ## Chl3 1 0.105 0.105 8.4580 0.003679 ## NBI4 1 73.486 73.486 5940.1653 < 2.2e-16 ## Anth4:Chl3 1 0.438 0.438 35.4071 3.213e-09 ## Anth4:NBI4 1 0.121 0.121 9.7847 0.001788 ## Chl3:NBI4 1 0.295 0.295 23.8417 1.139e-06

Anth4:Chl3:NBI4 1 0.028 0.028 2.2925 0.130175

Residuals 1784 22.070 0.012

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: NBI4

Df Sum Sq Mean Sq F value Pr(>F)

Anth4 1 12013 12013 1.5967e+04 < 2.2e-16 ## Chl3 1 95398 95398 1.2679e+05 < 2.2e-16

Flav5 1 9255 9255 1.2300e+04 < 2.2e-16 ## Anth4:Chl3 1 36 36 4.7348e+01 8.199e-12

Anth4:Flav5 1 118 118 1.5676e+02 < 2.2e-16 ## Chl3:Flav5 1 1393 1393 1.8521e+03 < 2.2e-16

Anth4:Chl3:Flav5 1 1 1 1.6591e+00 0.1979

Residuals 1784 1342 1

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1


#### Flowering

<img src="Analysis_files/figure-html/unnamed-chunk-7-1.png" width="672" />

Analysis of Variance Table

Response: Dat$Anth4 ## Df Sum Sq Mean Sq F value Pr(>F) ## Dat$Flower 1 0.0002 0.00023977 0.1325 0.7159

Residuals 1790 3.2393 0.00180967

Analysis of Variance Table

Response: Dat$Chl3 ## Df Sum Sq Mean Sq F value Pr(>F) ## Dat$Flower 1 37 36.997 0.1701 0.6801

Residuals 1790 389333 217.505

Analysis of Variance Table

Response: Dat$Flav5 ## Df Sum Sq Mean Sq F value Pr(>F) ## Dat$Flower 1 0.248 0.247514 4.4866 0.0343 * ## Residuals 1790 98.750 0.055168
## — ## Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: Dat$NBI4 ## Df Sum Sq Mean Sq F value Pr(>F) ## Dat$Flower 1 12 11.938 0.1788 0.6725

Residuals 1790 119544 66.784

``` #### Looking at Environment and Height

ManukaAnalysis

Melissa Taane

06/05/2019

F1 Layout

Height Diagram

Import and Arrange Data

Replicate Analysis

Replicate Data Overview

Replicate Group means

Anthocyanin Replicate Plots

ANOVA of Replicates

Allocation Analysis

Allocation Group Means

Boxplot comparing medians and measurement distributions

ANOVAs

Parent Tree Analysis

Anthocyanin Parent Plots

Chlorophyll Parent Plots

Flavonol Parent Plots

Nitrogen Parent Plots

Parental Cross Analysis (F1 Generation)

# A tibble: 3 x 5

Height Anth Chl Flav NBI

1 H 0.105 33.9 1.93 17.9

2 L 0.0968 35.6 1.98 18.5

3 M 0.0913 35.3 1.98 18.1

Analysis of Variance Table

Response: F1\(Anth ## Df Sum Sq Mean Sq F value Pr(>F) ## F1\)Height 2 0.0526 0.0262759 13.533 1.468e-06 *** ## Residuals 1789 3.4735 0.0019416 ## — ## Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Call:

lm(formula = F1\(Anth ~ F1\)Height)

Residuals:

Min 1Q Median 3Q Max

-0.10400 -0.02878 -0.00056 0.02666 0.37800

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 0.105002 0.001973 53.232 < 2e-16 ## F1\(HeightL -0.008225 0.002622 -3.137 0.00173 ** ## F1\)HeightM -0.013666 0.002630 -5.197 2.26e-07

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Residual standard error: 0.04406 on 1789 degrees of freedom

Multiple R-squared: 0.0149, Adjusted R-squared: 0.0138

F-statistic: 13.53 on 2 and 1789 DF, p-value: 1.468e-06

Analysis of Variance Table

Response: F1\(Chl ## Df Sum Sq Mean Sq F value Pr(>F) ## F1\)Height 2 853 426.30 1.8605 0.1559

Residuals 1789 409911 229.13

Analysis of Variance Table

Response: F1\(Flav ## Df Sum Sq Mean Sq F value Pr(>F) ## F1\)Height 2 0.915 0.45758 7.4943 0.0005739 *** ## Residuals 1789 109.232 0.06106 ## — ## Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Analysis of Variance Table

Response: F1\(NBI ## Df Sum Sq Mean Sq F value Pr(>F) ## F1\)Height 2 100 50.044 0.6992 0.4971

Residuals 1789 128039 71.570

# A tibble: 6 x 5

Row Chl NBI Anth Flav

1 1 35.6 17.3 0.107 2.09

2 10 30.5 15.7 0.112 1.98

3 11 29.9 15.0 0.112 2.04

4 12 34.6 18.7 0.0968 1.84

5 13 31.3 17.5 0.107 1.86

6 14 35.0 17.9 0.0888 1.98

Row Chl NBI Anth

17 :1 Min. :32.13 Min. :17.14 Min. :0.1163

0 :0 1st Qu.:32.13 1st Qu.:17.14 1st Qu.:0.1163

1 :0 Median :32.13 Median :17.14 Median :0.1163

10 :0 Mean :32.13 Mean :17.14 Mean :0.1163

11 :0 3rd Qu.:32.13 3rd Qu.:17.14 3rd Qu.:0.1163

12 :0 Max. :32.13 Max. :17.14 Max. :0.1163

(Other):0

Flav

Min. :1.887

1st Qu.:1.887

Median :1.887

Mean :1.887

3rd Qu.:1.887

Max. :1.887

Row Chl NBI Anth

1 : 1 Min. :29.19 Min. :14.38 Min. :0.07868

10 : 1 1st Qu.:32.02 1st Qu.:16.44 1st Qu.:0.08535

11 : 1 Median :34.38 Median :17.81 Median :0.09354

14 : 1 Mean :34.97 Mean :18.12 Mean :0.09677

15 : 1 3rd Qu.:37.05 3rd Qu.:19.46 3rd Qu.:0.10917

16 : 1 Max. :43.89 Max. :23.16 Max. :0.12239

(Other):33

Response: F1\(Anth ## Df Sum Sq Mean Sq F value Pr(>F) ## F1\)Height 2 0.0526 0.0262759 13.533 1.468e-06 * ## Residuals 1789 3.4735 0.0019416
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

(Intercept) 0.105002 0.001973 53.232 < 2e-16 *## F1\(HeightL -0.008225 0.002622 -3.137 0.00173 ## F1\)HeightM -0.013666 0.002630 -5.197 2.26e-07***

Response: F1\(Flav ## Df Sum Sq Mean Sq F value Pr(>F) ## F1\)Height 2 0.915 0.45758 7.4943 0.0005739 * ## Residuals 1789 109.232 0.06106
## — ## Signif. codes: 0 ‘’ 0.001 ’**’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1